About the book
This book aims to introduce the recent developments on theories, methods, algorithms, and applications of Bayesian inference, including variational Bayesian estimation, Bayesian variable selection or model selection, empirical Bayesian inference, objective Bayesian inference, Bayesian classification or clustering or discrimination, Bayesian deep learning and Bayesian reinforcement learning, robust Bayesian inference, Bayesian case deletion influence analysis, and Bayesian local influence analysis. Other covered topics will include Bayesian sensitivity analysis for high dimensional or ultrahigh dimensional complicated data such as categorical, ordered, and continuous data as well as imaging data, text data, repeated measurement data, or longitudinal data, or panel data, dynamic data, missing data, and so on. Various models such as probabilistic graphical models, generalized estimating equations, generalized moment models, parametric or nonparametric or semiparametric models, latent variable models, multivariate multilevel models, hidden Markov models, dynamic factor models, tensor regression models are also welcome. In particular, this book intends to demonstrate the application of Bayesian inference methods to artificial intelligence, medical imaging analysis, and topological data analysis, among others.